Air Conditioner Model Calibration Using Dynamic Parameter Segmentation
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Solution Overview
Problem
The existing method for calibrating air conditioner models requires a significant amount of time due to the large number of parameters involved, which can be inefficient for maintenance workers, especially in situations with limited technical skills or resources.
Innovation Solution
A failure diagnosis apparatus that selects and adjusts specific parameters of the air conditioner model based on the condition of the air conditioner, using a simulation and data comparison to reduce the number of calibration parameters and speed up the calibration process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the air conditioner model is calibrated using the technique disclosed in Patent Literature 1, then the calibration accuracy can be improved, but the time required for calibration increases significantly
Solution Approach 1:
The patent segments the large set of air conditioner model parameters into multiple adjustment parameter groups, where each group contains a subset of parameters to be adjusted. This segmentation allows the calibration process to focus on smaller parameter sets sequentially or selectively, reducing the overall calibration time while maintaining accuracy. The parameter selection unit divides the comprehensive parameter space into manageable groups based on different operating conditions.
Solution Approach 2:
The patent implements dynamic parameter selection by adjusting which parameters are calibrated based on the current operating state of the air conditioner. The parameter selection unit dynamically determines the appropriate adjustment parameter group according to real-time conditions, allowing the calibration process to adapt to varying operational contexts. This dynamic approach avoids calibrating all parameters uniformly, thereby reducing unnecessary calibration time while preserving accuracy for relevant parameters.
2Measurement precision
If all parameters of the air conditioner model are adjusted during calibration, then the model accuracy is improved, but the complexity of the calibration process increases
Solution Approach 1:
The calibration process complexity is reduced by segmenting all model parameters into multiple adjustment parameter groups. Each group contains a specific subset of parameters relevant to certain operating conditions. This segmentation simplifies the calibration process by allowing the system to work with smaller parameter sets at a time, rather than managing all parameters simultaneously, thus reducing process complexity while maintaining comprehensive model accuracy.
Solution Approach 2:
The patent applies local quality by adjusting different parameter groups based on specific operating conditions. Instead of uniformly adjusting all parameters regardless of context, the system selectively adjusts parameters that are locally relevant to the current operating state. This approach reduces calibration complexity by focusing computational resources on locally important parameters while maintaining overall model accuracy.
3Ease of operation
If the parameter selection is performed without considering the state of the air conditioner, then the calibration process is simplified, but the applicability of the calibration results decreases
Solution Approach 1:
The patent implements dynamic parameter selection that adapts to the current state of the air conditioner. The parameter selection unit determines which adjustment parameter group to use based on real-time operating conditions such as temperature, humidity, and system load. This dynamic adaptation ensures that the calibration results are highly applicable to the specific operating context while maintaining reasonable process simplicity through automated state-based selection.
Solution Approach 2:
The system changes the selected parameters for calibration based on the detected operating state of the air conditioner. Different parameter groups are activated depending on whether the system is in cooling mode, heating mode, or other operating conditions. This parameter change strategy maintains calibration applicability to various operating scenarios while keeping the process simple through automated parameter selection based on state detection.
Data Source
AI summary
A failure diagnosis apparatus (100) calibrates an air conditioner model (191) by adjusting a value of a parameter of the air conditioner model (191) used when a simulation of operation of an air conditioner (200) is executed, and also includes a parameter selection unit (130). The parameter selection unit (130) selects from among parameters of the air conditioner model (191), each parameter that forms an adjustment parameter group that consists of at least one parameter whose value is a candidate to be adjusted when the air conditioner model (191) is calibrated based on a condition according to a state of the air conditioner (200).